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Message: <blockquote data-quote="Aki992" data-source="post: 31347893" data-attributes="member: 557018">[MEDIA=youtube]k1njvbBmfsw[/MEDIA] <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=124s" target="_blank">02:04</a> Explaining Fine-tuning and Its Limitations, Emphasizing the Need to Avoid It When Possible   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=140s&pp=0gcJCTAAlc8ueATH" target="_blank">02:20</a> Introduction to the Principles and Applications of Retrieval-Augmented Generation (RAG)   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=156s" target="_blank">02:36</a> Discussion of the Definition and Examples of Agentic AI Workflows   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=203s" target="_blank">03:23</a> Brief Exploration of Multi-Agent Workflows and Future Prospects for AI   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=232s" target="_blank">03:52</a> Open-Ended Question: What Limitations Does a Base Model Face When Used Alone?   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=579s" target="_blank">09:39</a> LLMs May Perform Poorly on Specific Tasks, Especially When Lacking Domain-Specific Knowledge   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=682s" target="_blank">11:22</a> Issues with LLMs Handling Limited Context and Challenges in Long-Text Processing   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=785s" target="_blank">13:05</a> Explaining the "Needle in a Haystack" Problem: Difficulties in Extracting Information from Large Texts   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=872s" target="_blank">14:32</a> Discussion of RAG: Its Advantages as an LLM Augmentation Mechanism and Long-Term Potential   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1002s" target="_blank">16:42</a> Two Main Dimensions for Improving LLMs: Base Model Enhancements vs. Application Engineering   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1091s" target="_blank">18:11</a> Beginning the Discussion on Prompt Engineering: Its Importance and Impact   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1309s" target="_blank">21:49</a> Basic Prompt Design Principles: Optimizing Output Through Clear Instructions   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1441s" target="_blank">24:01</a> Chain-of-Thought (CoT) Prompting: Breaking Down Tasks to Improve Model Performance   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1537s" target="_blank">25:37</a> Prompt Templates: Enabling Scalability and Personalized Applications   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1668s" target="_blank">27:48</a> Comparison and Applications of Zero-Shot vs. Few-Shot Prompting   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=1935s" target="_blank">32:15</a> Chaining Complex Prompts: Optimizing Workflows and Simplifying Debugging   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=2276s" target="_blank">37:56</a> Methods for Evaluating Prompts: Human Rating and Using LLMs as Judges   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=2480s" target="_blank">41:20</a> Drawbacks of Fine-Tuning: High Data Requirements, Overfitting, and Cost   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=2570s&pp=0gcJCTAAlc8ueATH" target="_blank">42:50</a> Advantages of Fine-Tuning: Suitable for Domains Requiring High Precision   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=2691s" target="_blank">44:51</a> Core Concepts of RAG and Its Role in Addressing LLM Limitations   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=2861s&pp=0gcJCTAAlc8ueATH" target="_blank">47:41</a> How RAG Works: Embeddings, Vector Databases, Retrieval, and Prompt Integration   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=3002s&pp=0gcJCTAAlc8ueATH" target="_blank">50:02</a> Advanced RAG Techniques, Such as Chunking and Hypothetical Document Embeddings (HyDE)   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=3233s&pp=0gcJCTAAlc8ueATH" target="_blank">53:53</a> Agentic AI Workflows: Moving Toward Autonomous and Specialized Systems   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=3479s" target="_blank">57:59</a> Paradigm Shift in Software Engineering: From Deterministic to Probabilistic Thinking   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=3831s" target="_blank">1:03:51</a> Enterprise Workflow Case Study: Using Generative AI Agents to Optimize Credit Risk Memos   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=4021s" target="_blank">1:07:01</a> Core Components of Agents: Prompts, Context Management (Memory), and Tools (APIs)   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=4217s" target="_blank">1:10:17</a> Different Levels of Agent Autonomy: From Hard-Coded Steps to Autonomously Creating Tools   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=4340s" target="_blank">1:12:20</a> Model Context Protocol (MCP) vs. Traditional APIs and Its Advantages   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=4660s" target="_blank">1:17:40</a> Step-by-Step Execution Example: An Intelligent Travel Agent   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=4753s&pp=0gcJCTAAlc8ueATH" target="_blank">1:19:13</a> Evaluating Agentic AI Performance: End-to-End, Component-Level, Objective, and Subjective Metrics   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=5134s" target="_blank">1:25:34</a> Case Study: Building and Evaluating an AI Agent for Customer Support   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=5666s" target="_blank">1:34:26</a> Multi-Agent Workflows: Benefits of Parallel Processing and Component Reuse   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=5744s&pp=0gcJCTAAlc8ueATH" target="_blank">1:35:44</a> Case Study Discussion: Multi-Agent System Design for Smart Home Automation   <a href="https://www.youtube.com/watch?v=k1njvbBmfsw&t=6197s&pp=0gcJCTAAlc8ueATH" target="_blank">1:43:17</a> Future Trends in AI: Plateau Period, Architecture Search, Multimodality, and Multi-Method Collaborative Learning--Copied from a comment--</blockquote>

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